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\begin{document}
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\title{Project Paper: Villain LODE Artificial Intelligence eNhancement LODE-Villain (LODE Villain)}


% author names and affiliations
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\author{\IEEEauthorblockN{Jakob Huber}
\IEEEauthorblockA{University of Mannheim\\
Mannheim, Germany\\
jahuber@mail.uni-mannheim.de}
\and
\IEEEauthorblockN{Bernd Opitz}
\IEEEauthorblockA{University of Mannheim\\
Mannheim, Germany\\
jopitz@mail.uni-mannheim.de}
\and
\IEEEauthorblockN{Timo Sztyler}
\IEEEauthorblockA{University of Mannheim\\
Mannheim, Germany\\
tsztyler@mail.uni-mannheim.de}}

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%\author{\IEEEauthorblockN{Michael Shell\IEEEauthorrefmark{1},
%Homer Simpson\IEEEauthorrefmark{2},
%James Kirk\IEEEauthorrefmark{3}, 
%Montgomery Scott\IEEEauthorrefmark{3} and
%Eldon Tyrell\IEEEauthorrefmark{4}}
%\IEEEauthorblockA{\IEEEauthorrefmark{1}School of Electrical and Computer Engineering\\
%Georgia Institute of Technology,
%Atlanta, Georgia 30332--0250\\ Email: see http://www.michaelshell.org/contact.html}
%\IEEEauthorblockA{\IEEEauthorrefmark{2}Twentieth Century Fox, Springfield, USA\\
%Email: homer@thesimpsons.com}
%\IEEEauthorblockA{\IEEEauthorrefmark{3}Starfleet Academy, San Francisco, California 96678-2391\\
%Telephone: (800) 555--1212, Fax: (888) 555--1212}
%\IEEEauthorblockA{\IEEEauthorrefmark{4}Tyrell Inc., 123 Replicant Street, Los Angeles, California 90210--4321}}




% use for special paper notices
%\IEEEspecialpapernotice{(Invited Paper)}




% make the title area
\maketitle


\begin{abstract}
%\boldmath
This report summarizes our term project which is concerned with the artificial intelligence enhancement component ``\textit{Villain}'' for the linked open data enhancer LODE. The purpose of Villain is to support any kind of user in enhancing information. It does so by providing an automatic search that makes use of instance matching to find matching candidates that may contain additional information about a given instance.
\end{abstract}
\IEEEpeerreviewmaketitle

\section{Introduction}
% no\IEEEPARstart
%TODO - Beschreibung Open Link Data auf dem Vormarsch - structured data werden verwendet (web 3.0?)...
Linked open data (LOD) and its usage is on the rise in the past years. The idea behind LOD is to make raw data accessible and structuring it in a machine-readable fashion available through an open standard query language. As extension to the current ``Web 2.0'', it is a logical step to add semantic meta data and thereby bring the \textit{World Wide Web} closer to its full potential.\par
The motivation for the presented LODE AI component (\textit{Villain}) is to aid a user in linking data. The user should be able to improve linked data without any background information of technical parts, such as the data structures and what an ontology is. This paper describes the component \textit{Villain} for LODE which deals with the automatic search for instances that might provide more information for a selected instance. The proposals (matching candidates) should be closely interlinked regarding the domain of the instance, so that the user should be able to easily enhance an instance with information from other data sources. An instance is a manifestation of a given schema such as a person or book with different kinds of properties. These properties can be represented by simple data types such as strings or dates or by links to other instances which can eventually be modeled as a graph. This construct is the basis for our instance matching method. Thus, the purpose of \textit{Villain} is to provide the user with useful suggestions as to which instance from another data source is about the same subject. The idea for retrieving relevant instances from other data sources does not rely on the presence of \textit{sameAs} links, as these may not be present. Instead, the methods described in the next section use the aforementioned object and data properties to make informed choices as to which instance is relevant.\\
The paper is structured as follows: section \ref{sec:method} will present the methods which will be used to load, prepare and match the instances. Section \ref{sec:user interface} gives a brief overview of the user interface which \textit{Villain} provides for convenience and testing. Section \ref{sec:evaluation} is concerned with evaluating the performance of our methods based on test data with regard to two different data sources, and section \ref{sec:conclusion} highlights the important results and conclusions.

\input{method} 

\section{User Interface}
\label{sec:user interface}
Another part of our project is a website which makes our implemented method easily accessible. A user has the opportunity to search for something he is interested in. The web service presents instances from different sources which match the search term to the user. When the user selects an instance, it is then treated as the starting instance and is thus used to find instances from other resources. The matched instances as well as the respective confidence values are presented to the user on the result page (Fig.\ \ref{fig:result page}). The user then has the possibility to access the different resources of the matching candidates in order to check their correctness. This step simulates a use case scenario of our method and is also a possibility for evaluation. While we designed our application for ease of use with inexperienced users in mind, we did not evaluate whether usage is actually easy. The focus of our work was the application of artificial intelligence concepts, thus aspects concerning usability were out of scope and would have to be explored in future work.

\begin{figure}[H]
\centering
\includegraphics[width=0.485\textwidth]{ui-result-page.png}
\caption{A sample result page of our application.}
\label{fig:result page}
\end{figure}


\section{Evaluation}
\label{sec:evaluation}
\subsection{Procedure}
The basis of the evaluation is the data which is provided by Freebase\cite{freebase} and DBpedia\cite{dbpedia}. Both sources provide linked data in different formats, namely N-Triples and JSON. As already mentioned in section \ref{sec:method}, we transform these different formats to the same model to make them comparable. Most of the instances have the property ``\textit{sameAs}'' which contains a set of values which link to other instances, also instances that are provided by Freebase or DBpedia. This allows us to verify whether the automatic search (or rather the instance matching) found the correct instance provided by another source. We generated a set of 50\,000 pairs, where a pair represents two URIs which point to the same instance on DBpedia and Freebase respectively. Furthermore, all instances we chose are of the type person. The reason for the selected type of instances is justified by the fact that persons have a lot of relationships such as ``parentOf'' or ``influencedBy'' which allows to evaluate our methods that are based on the graph structure. For evaluation, we took random samples from the set of 50\,000 instances and tried to match it.

\subsection{Result}
The result of the investigation regarding our test data set reveals a lot of different aspects, problems and insights. Thus, we investigate our described methods separately as well as on a whole. In order to measure the performance of our methods we need to define some conditions which the results have to fulfill. Our goal is to present a limited set of matching candidates to the user out of which he can easily pick the correct match. Thus, we define that the algorithm finds the correct match if it is among the five highest-valued ones as this still manageable for a user. The results reveal that our approach returns the correct result within the top-5 in more than 90\% of the cases. The fact that the correct one has the highest rating in more then 80\% of the cases is even more satisfying because it allows to reduce the returned instances to less than five thus increasing the usability.
\begin{figure}[H]
\centering
\includegraphics[width=0.485\textwidth]{eval.pdf}
\caption{The evaluation of the matching on 300 instances (for both directions)}
\label{fig:eval}
\end{figure}
The main result of the evaluation of the matching process is presented in figure \ref{fig:eval}. The $x$-axis shows the source of the initial instance $I$, thus matching candidates $MC$ were searched in the respective source. The $y$-axis shows the percentage of instances falling into one of ``correct'', ``top-5'' or ``missing''. A $MC$ is considered to be ``correct'' if the $MC$ that is the same as $I$ received the highest similarity value. ``top-5'' means that the correct $MC$ did not achieve the highest level of similarity but was in the top-5. If the correct $MC$ did not occur in the top-5, it was considered to be ``missing''.\par
Moreover, we discovered that it is not really beneficial to use complex methods to determine the final set of matching candidates. They do not affect the results significantly, but increase the computation time. Thus, we are not able to prove that the application of graph based approaches is reasonable in the context of linked data. At the very least, our \textit{lightweight graph-based matching} method was not capable of improving the results of the \textit{simple matching} within our test data set but increased the computation time considerably. In some cases, the computation of the proposals in the \textit{lightweight graph-based matching} takes so long that practical use is infeasible.\par
In general, more complex methods such as the comparison of the properties and values take a lot of time for loading the data as well as the comparison of it since string matching is expensive. Hence, the long runtime does not allow to use complex methods as part of real-time applications. A major problem was the slow processing of queries by our data providers which is the same for every theoretically possible approach. Thus, it depends on the application whether a long runtime is appropriate and complex methods are an option.

\subsection{Remarks}
Most of the problems and insights in the result section can be reduced to the test data set. Thus, one major problem was that most of our pairs were trivial. That is to say that in most cases the name or ID of an instance was enough to find the searched instance. Therefore, this does not allow us to draw conclusions about the graph structure provided by the instances. To investigate the effects of the graph-based matching, it would have been necessary to have non-trivial pairs. A second test data set would have to be manually created by a person who has enough domain knowledge to detect those instances. In this context, we developed a web interface (see section \ref{sec:user interface}) which allows to search for instances and their $MC$s to investigate this problem, but eventually we did not find any suitable pairs.\par
Independently, it could be possible to improve the graph matching by searching with multiple instances which have relations among each other instead of a single instance. This would allow to search a set of $MC$s for each instance, so that eventually we can also try to build a graph which has the most similar structure to the original one. In this context, we could use and extend the described methods in section \ref{sec:method} to calculate different weights for the existing or missing properties but eventually it would increase the runtime many times over.\par
Finally, we only investigate instance matching in the context of string matching and simple graph matching but we did not consider possibilities to evaluate whether two properties which have the same label also convey the same meaning, i.e.\ the same semantics.

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\section{Conclusion}
\label{sec:conclusion}
The purpose of our project is to find matching candidates from different linked data sources. We load an instance from a resource and try to find other instances which describe the same entity. To do so, we use basic information about the first instance to retrieve a set of matching candidates. This set is reduced in multiple steps by using string similarity measures. An additional step takes the structure of the linked data into account by not only considering the directly connected values but also the values of connected objects. We evaluated our method with data sets containing links between DBpedia and Freebase. Good results were already achieved with relatively simple methods of comparison. Additional or complex methods that take the structure of the graph into account could not improve these results significantly. The positive aspect of this is that the more complex methods take significantly longer and thus may be unsuitable for practical application. Another noteworthy result is the size of the matching candidates from which the user has to select the correct one. The returned matching candidates can be reduced to a manageable size which underlines the practical usefulness of our approach. A weak point is that our test data set is not necessarily optimal because it does not contain a lot of complex pairs. Thus, our evaluation may not be representative for the performance in other or more complex domains.

\section*{Project}
All project, result and evaluation data are available at \url{https://code.google.com/p/lode-villain/}. We started to develop the project as a web application by using the \textit{play!}\ Framework but the main features do not use any libraries of \textit{play!}\ and are runnable in every Java 1.7 environment.

\section*{Acknowledgment}
We would like to thank Mathias Niepert, Rim Helaoui and Jan Noessner for the inspiring lectures, the motivating course recitations and their continuous support and advice.

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